Overview

Dataset statistics

Number of variables17
Number of observations15289
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)< 0.1%
Total size in memory2.0 MiB
Average record size in memory136.0 B

Variable types

Numeric14
Categorical3

Alerts

Dataset has 7 (< 0.1%) duplicate rowsDuplicates
clonesize is highly overall correlated with honeybeeHigh correlation
honeybee is highly overall correlated with clonesizeHigh correlation
MaxOfUpperTRange is highly overall correlated with MaxOfLowerTRange and 4 other fieldsHigh correlation
MaxOfLowerTRange is highly overall correlated with MaxOfUpperTRange and 4 other fieldsHigh correlation
MinOfLowerTRange is highly overall correlated with MaxOfUpperTRange and 4 other fieldsHigh correlation
RainingDays is highly overall correlated with AverageRainingDays and 1 other fieldsHigh correlation
AverageRainingDays is highly overall correlated with RainingDays and 1 other fieldsHigh correlation
fruitset is highly overall correlated with fruitmass and 2 other fieldsHigh correlation
fruitmass is highly overall correlated with fruitset and 2 other fieldsHigh correlation
seeds is highly overall correlated with fruitset and 2 other fieldsHigh correlation
yield is highly overall correlated with RainingDays and 4 other fieldsHigh correlation
MinOfUpperTRange is highly overall correlated with MaxOfUpperTRange and 4 other fieldsHigh correlation
AverageOfUpperTRange is highly overall correlated with MaxOfUpperTRange and 4 other fieldsHigh correlation
AverageOfLowerTRange is highly overall correlated with MaxOfUpperTRange and 4 other fieldsHigh correlation
honeybee is highly skewed (γ1 = 41.61324372)Skewed

Reproduction

Analysis started2023-05-14 08:48:05.238513
Analysis finished2023-05-14 08:48:16.716336
Duration11.48 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

clonesize
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.70469
Minimum10
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:16.745092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12.5
Q112.5
median25
Q325
95-th percentile25
Maximum40
Range30
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation6.5952109
Coefficient of variation (CV)0.3347026
Kurtosis-1.2183377
Mean19.70469
Median Absolute Deviation (MAD)0
Skewness0.04986072
Sum301265
Variance43.496806
MonotonicityNot monotonic
2023-05-14T14:18:16.786278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
25 8245
53.9%
12.5 6717
43.9%
37.5 265
 
1.7%
20 56
 
0.4%
10 4
 
< 0.1%
40 2
 
< 0.1%
ValueCountFrequency (%)
10 4
 
< 0.1%
12.5 6717
43.9%
20 56
 
0.4%
25 8245
53.9%
37.5 265
 
1.7%
40 2
 
< 0.1%
ValueCountFrequency (%)
40 2
 
< 0.1%
37.5 265
 
1.7%
25 8245
53.9%
20 56
 
0.4%
12.5 6717
43.9%
10 4
 
< 0.1%

honeybee
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38931428
Minimum0
Maximum18.43
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:16.829813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.5
Q30.5
95-th percentile0.5
Maximum18.43
Range18.43
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.3616431
Coefficient of variation (CV)0.92892329
Kurtosis2040.7378
Mean0.38931428
Median Absolute Deviation (MAD)0
Skewness41.613244
Sum5952.226
Variance0.13078573
MonotonicityNot monotonic
2023-05-14T14:18:16.872618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.5 7832
51.2%
0.25 7285
47.6%
0.75 110
 
0.7%
0.537 38
 
0.2%
0 16
 
0.1%
18.43 5
 
< 0.1%
6.64 3
 
< 0.1%
ValueCountFrequency (%)
0 16
 
0.1%
0.25 7285
47.6%
0.5 7832
51.2%
0.537 38
 
0.2%
0.75 110
 
0.7%
6.64 3
 
< 0.1%
18.43 5
 
< 0.1%
ValueCountFrequency (%)
18.43 5
 
< 0.1%
6.64 3
 
< 0.1%
0.75 110
 
0.7%
0.537 38
 
0.2%
0.5 7832
51.2%
0.25 7285
47.6%
0 16
 
0.1%

bumbles
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28676768
Minimum0
Maximum0.585
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:16.921094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.25
Q30.38
95-th percentile0.38
Maximum0.585
Range0.585
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.059916909
Coefficient of variation (CV)0.20893885
Kurtosis-0.66936043
Mean0.28676768
Median Absolute Deviation (MAD)0
Skewness0.81557068
Sum4384.391
Variance0.003590036
MonotonicityNot monotonic
2023-05-14T14:18:16.965104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.25 10856
71.0%
0.38 4376
28.6%
0.117 39
 
0.3%
0 5
 
< 0.1%
0.042 3
 
< 0.1%
0.058 2
 
< 0.1%
0.065 2
 
< 0.1%
0.585 2
 
< 0.1%
0.293 2
 
< 0.1%
0.56 1
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
0.042 3
 
< 0.1%
0.058 2
 
< 0.1%
0.065 2
 
< 0.1%
0.117 39
 
0.3%
0.25 10856
71.0%
0.26 1
 
< 0.1%
0.293 2
 
< 0.1%
0.38 4376
28.6%
0.56 1
 
< 0.1%
ValueCountFrequency (%)
0.585 2
 
< 0.1%
0.56 1
 
< 0.1%
0.38 4376
28.6%
0.293 2
 
< 0.1%
0.26 1
 
< 0.1%
0.25 10856
71.0%
0.117 39
 
0.3%
0.065 2
 
< 0.1%
0.058 2
 
< 0.1%
0.042 3
 
< 0.1%

andrena
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49267539
Minimum0
Maximum0.75
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.013839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.38
median0.5
Q30.63
95-th percentile0.75
Maximum0.75
Range0.75
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.14811502
Coefficient of variation (CV)0.30063409
Kurtosis-0.82178331
Mean0.49267539
Median Absolute Deviation (MAD)0.12
Skewness0.16204692
Sum7532.514
Variance0.021938059
MonotonicityNot monotonic
2023-05-14T14:18:17.059413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.38 4565
29.9%
0.5 4165
27.2%
0.63 3042
19.9%
0.75 1828
12.0%
0.25 1624
 
10.6%
0.409 43
 
0.3%
0 8
 
0.1%
0.229 4
 
< 0.1%
0.49 2
 
< 0.1%
0.147 2
 
< 0.1%
Other values (6) 6
 
< 0.1%
ValueCountFrequency (%)
0 8
 
0.1%
0.101 1
 
< 0.1%
0.147 2
 
< 0.1%
0.229 4
 
< 0.1%
0.234 1
 
< 0.1%
0.235 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1624
 
10.6%
0.38 4565
29.9%
0.409 43
 
0.3%
ValueCountFrequency (%)
0.75 1828
12.0%
0.707 1
 
< 0.1%
0.63 3042
19.9%
0.56 1
 
< 0.1%
0.5 4165
27.2%
0.49 2
 
< 0.1%
0.409 43
 
0.3%
0.38 4565
29.9%
0.25 1624
 
10.6%
0.24 1
 
< 0.1%

osmia
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59235548
Minimum0
Maximum0.75
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.109219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.5
median0.63
Q30.75
95-th percentile0.75
Maximum0.75
Range0.75
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.13948903
Coefficient of variation (CV)0.23548196
Kurtosis0.61946823
Mean0.59235548
Median Absolute Deviation (MAD)0.12
Skewness-0.84519564
Sum9056.523
Variance0.019457189
MonotonicityNot monotonic
2023-05-14T14:18:17.153886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.63 4763
31.2%
0.5 4699
30.7%
0.75 4387
28.7%
0.25 872
 
5.7%
0.38 509
 
3.3%
0.058 42
 
0.3%
0 6
 
< 0.1%
0.021 4
 
< 0.1%
0.117 2
 
< 0.1%
0.62 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 6
 
< 0.1%
0.02 1
 
< 0.1%
0.021 4
 
< 0.1%
0.058 42
 
0.3%
0.078 1
 
< 0.1%
0.117 2
 
< 0.1%
0.25 872
 
5.7%
0.38 509
 
3.3%
0.5 4699
30.7%
0.585 1
 
< 0.1%
ValueCountFrequency (%)
0.75 4387
28.7%
0.63 4763
31.2%
0.62 1
 
< 0.1%
0.606 1
 
< 0.1%
0.585 1
 
< 0.1%
0.5 4699
30.7%
0.38 509
 
3.3%
0.25 872
 
5.7%
0.117 2
 
< 0.1%
0.078 1
 
< 0.1%

MaxOfUpperTRange
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.169887
Minimum69.7
Maximum94.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.200082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum69.7
5-th percentile69.7
Q177.4
median86
Q386
95-th percentile94.6
Maximum94.6
Range24.9
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation9.1467025
Coefficient of variation (CV)0.11131453
Kurtosis-1.3347433
Mean82.169887
Median Absolute Deviation (MAD)8.6
Skewness0.0073588935
Sum1256295.4
Variance83.662167
MonotonicityNot monotonic
2023-05-14T14:18:17.243347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
86 4200
27.5%
77.4 3788
24.8%
94.6 3734
24.4%
69.7 3564
23.3%
89 2
 
< 0.1%
79 1
 
< 0.1%
ValueCountFrequency (%)
69.7 3564
23.3%
77.4 3788
24.8%
79 1
 
< 0.1%
86 4200
27.5%
89 2
 
< 0.1%
94.6 3734
24.4%
ValueCountFrequency (%)
94.6 3734
24.4%
89 2
 
< 0.1%
86 4200
27.5%
79 1
 
< 0.1%
77.4 3788
24.8%
69.7 3564
23.3%

MinOfUpperTRange
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.6 KiB
52.0
4200 
46.8
3788 
57.2
3736 
42.1
3562 
39.0
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters61156
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42.1
2nd row42.1
3rd row52.0
4th row46.8
5th row46.8

Common Values

ValueCountFrequency (%)
52.0 4200
27.5%
46.8 3788
24.8%
57.2 3736
24.4%
42.1 3562
23.3%
39.0 3
 
< 0.1%

Length

2023-05-14T14:18:17.289845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-14T14:18:17.351416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
52.0 4200
27.5%
46.8 3788
24.8%
57.2 3736
24.4%
42.1 3562
23.3%
39.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 15289
25.0%
2 11498
18.8%
5 7936
13.0%
4 7350
12.0%
0 4203
 
6.9%
6 3788
 
6.2%
8 3788
 
6.2%
7 3736
 
6.1%
1 3562
 
5.8%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45867
75.0%
Other Punctuation 15289
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11498
25.1%
5 7936
17.3%
4 7350
16.0%
0 4203
 
9.2%
6 3788
 
8.3%
8 3788
 
8.3%
7 3736
 
8.1%
1 3562
 
7.8%
3 3
 
< 0.1%
9 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15289
25.0%
2 11498
18.8%
5 7936
13.0%
4 7350
12.0%
0 4203
 
6.9%
6 3788
 
6.2%
8 3788
 
6.2%
7 3736
 
6.1%
1 3562
 
5.8%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15289
25.0%
2 11498
18.8%
5 7936
13.0%
4 7350
12.0%
0 4203
 
6.9%
6 3788
 
6.2%
8 3788
 
6.2%
7 3736
 
6.1%
1 3562
 
5.8%
3 3
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.6 KiB
71.9
4200 
64.7
3787 
79.0
3735 
58.2
3564 
65.6
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters61156
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row58.2
2nd row58.2
3rd row71.9
4th row64.7
5th row64.7

Common Values

ValueCountFrequency (%)
71.9 4200
27.5%
64.7 3787
24.8%
79.0 3735
24.4%
58.2 3564
23.3%
65.6 3
 
< 0.1%

Length

2023-05-14T14:18:17.401803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-14T14:18:17.455676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
71.9 4200
27.5%
64.7 3787
24.8%
79.0 3735
24.4%
58.2 3564
23.3%
65.6 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 15289
25.0%
7 11722
19.2%
9 7935
13.0%
1 4200
 
6.9%
6 3793
 
6.2%
4 3787
 
6.2%
0 3735
 
6.1%
5 3567
 
5.8%
8 3564
 
5.8%
2 3564
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45867
75.0%
Other Punctuation 15289
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 11722
25.6%
9 7935
17.3%
1 4200
 
9.2%
6 3793
 
8.3%
4 3787
 
8.3%
0 3735
 
8.1%
5 3567
 
7.8%
8 3564
 
7.8%
2 3564
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 15289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15289
25.0%
7 11722
19.2%
9 7935
13.0%
1 4200
 
6.9%
6 3793
 
6.2%
4 3787
 
6.2%
0 3735
 
6.1%
5 3567
 
5.8%
8 3564
 
5.8%
2 3564
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15289
25.0%
7 11722
19.2%
9 7935
13.0%
1 4200
 
6.9%
6 3793
 
6.2%
4 3787
 
6.2%
0 3735
 
6.1%
5 3567
 
5.8%
8 3564
 
5.8%
2 3564
 
5.8%

MaxOfLowerTRange
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.229538
Minimum50.2
Maximum68.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.501270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.2
5-th percentile50.2
Q155.8
median62
Q362
95-th percentile68.2
Maximum68.2
Range18
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation6.6106396
Coefficient of variation (CV)0.11161052
Kurtosis-1.3333676
Mean59.229538
Median Absolute Deviation (MAD)6.2
Skewness0.0024495769
Sum905560.4
Variance43.700556
MonotonicityNot monotonic
2023-05-14T14:18:17.542764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
62 4199
27.5%
55.8 3787
24.8%
68.2 3736
24.4%
50.2 3563
23.3%
66 3
 
< 0.1%
52 1
 
< 0.1%
ValueCountFrequency (%)
50.2 3563
23.3%
52 1
 
< 0.1%
55.8 3787
24.8%
62 4199
27.5%
66 3
 
< 0.1%
68.2 3736
24.4%
ValueCountFrequency (%)
68.2 3736
24.4%
66 3
 
< 0.1%
62 4199
27.5%
55.8 3787
24.8%
52 1
 
< 0.1%
50.2 3563
23.3%

MinOfLowerTRange
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.660553
Minimum24.3
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.589444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile24.3
Q127
median30
Q330
95-th percentile33
Maximum33
Range8.7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1953666
Coefficient of variation (CV)0.11149005
Kurtosis-1.3337859
Mean28.660553
Median Absolute Deviation (MAD)3
Skewness0.0048458494
Sum438191.2
Variance10.210368
MonotonicityNot monotonic
2023-05-14T14:18:17.630431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
30 4199
27.5%
27 3787
24.8%
33 3735
24.4%
24.3 3564
23.3%
28 2
 
< 0.1%
25 1
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
24.3 3564
23.3%
25 1
 
< 0.1%
27 3787
24.8%
28 2
 
< 0.1%
30 4199
27.5%
31 1
 
< 0.1%
33 3735
24.4%
ValueCountFrequency (%)
33 3735
24.4%
31 1
 
< 0.1%
30 4199
27.5%
28 2
 
< 0.1%
27 3787
24.8%
25 1
 
< 0.1%
24.3 3564
23.3%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.6 KiB
50.8
4200 
45.8
3787 
55.9
3735 
41.2
3564 
45.3
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters61156
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41.2
2nd row41.2
3rd row50.8
4th row45.8
5th row45.8

Common Values

ValueCountFrequency (%)
50.8 4200
27.5%
45.8 3787
24.8%
55.9 3735
24.4%
41.2 3564
23.3%
45.3 3
 
< 0.1%

Length

2023-05-14T14:18:17.677411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-14T14:18:17.733787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
50.8 4200
27.5%
45.8 3787
24.8%
55.9 3735
24.4%
41.2 3564
23.3%
45.3 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
5 15460
25.3%
. 15289
25.0%
8 7987
13.1%
4 7354
12.0%
0 4200
 
6.9%
9 3735
 
6.1%
1 3564
 
5.8%
2 3564
 
5.8%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45867
75.0%
Other Punctuation 15289
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 15460
33.7%
8 7987
17.4%
4 7354
16.0%
0 4200
 
9.2%
9 3735
 
8.1%
1 3564
 
7.8%
2 3564
 
7.8%
3 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 15460
25.3%
. 15289
25.0%
8 7987
13.1%
4 7354
12.0%
0 4200
 
6.9%
9 3735
 
6.1%
1 3564
 
5.8%
2 3564
 
5.8%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 15460
25.3%
. 15289
25.0%
8 7987
13.1%
4 7354
12.0%
0 4200
 
6.9%
9 3735
 
6.1%
1 3564
 
5.8%
2 3564
 
5.8%
3 3
 
< 0.1%

RainingDays
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.660865
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.779485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q116
median16
Q324
95-th percentile34
Maximum34
Range33
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.657582
Coefficient of variation (CV)0.6247075
Kurtosis-1.0760813
Mean18.660865
Median Absolute Deviation (MAD)8
Skewness-0.26196767
Sum285305.96
Variance135.89922
MonotonicityNot monotonic
2023-05-14T14:18:17.820248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
16 4361
28.5%
24 3837
25.1%
34 3521
23.0%
1 3521
23.0%
3.77 48
 
0.3%
26 1
 
< 0.1%
ValueCountFrequency (%)
1 3521
23.0%
3.77 48
 
0.3%
16 4361
28.5%
24 3837
25.1%
26 1
 
< 0.1%
34 3521
23.0%
ValueCountFrequency (%)
34 3521
23.0%
26 1
 
< 0.1%
24 3837
25.1%
16 4361
28.5%
3.77 48
 
0.3%
1 3521
23.0%

AverageRainingDays
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32417621
Minimum0.06
Maximum0.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.862166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.1
Q10.26
median0.26
Q30.39
95-th percentile0.56
Maximum0.56
Range0.5
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.16390481
Coefficient of variation (CV)0.50560407
Kurtosis-1.1724067
Mean0.32417621
Median Absolute Deviation (MAD)0.13
Skewness0.081862953
Sum4956.33
Variance0.026864786
MonotonicityNot monotonic
2023-05-14T14:18:17.904901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.26 4361
28.5%
0.39 3837
25.1%
0.1 3520
23.0%
0.56 3519
23.0%
0.06 49
 
0.3%
0.25 1
 
< 0.1%
0.07 1
 
< 0.1%
0.14 1
 
< 0.1%
ValueCountFrequency (%)
0.06 49
 
0.3%
0.07 1
 
< 0.1%
0.1 3520
23.0%
0.14 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 4361
28.5%
0.39 3837
25.1%
0.56 3519
23.0%
ValueCountFrequency (%)
0.56 3519
23.0%
0.39 3837
25.1%
0.26 4361
28.5%
0.25 1
 
< 0.1%
0.14 1
 
< 0.1%
0.1 3520
23.0%
0.07 1
 
< 0.1%
0.06 49
 
0.3%

fruitset
Real number (ℝ)

Distinct1526
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50274089
Minimum0.19273166
Maximum0.65214409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:17.956457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.19273166
5-th percentile0.37667015
Q10.45824644
median0.50659971
Q30.56044524
95-th percentile0.6126334
Maximum0.65214409
Range0.45941243
Interquartile range (IQR)0.1021988

Descriptive statistics

Standard deviation0.074389601
Coefficient of variation (CV)0.14796807
Kurtosis-0.1697318
Mean0.50274089
Median Absolute Deviation (MAD)0.051794329
Skewness-0.42660562
Sum7686.4055
Variance0.0055338127
MonotonicityNot monotonic
2023-05-14T14:18:18.016954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.583378727 96
 
0.6%
0.566319234 81
 
0.5%
0.534251703 63
 
0.4%
0.542170237 60
 
0.4%
0.48180056 59
 
0.4%
0.473458531 58
 
0.4%
0.4965362 58
 
0.4%
0.603524723 56
 
0.4%
0.392583006 56
 
0.4%
0.587319779 56
 
0.4%
Other values (1516) 14646
95.8%
ValueCountFrequency (%)
0.192731658 1
 
< 0.1%
0.226568004 1
 
< 0.1%
0.233554492 9
0.1%
0.23591617 1
 
< 0.1%
0.237873564 1
 
< 0.1%
0.246568004 10
0.1%
0.249334678 8
0.1%
0.253673466 1
 
< 0.1%
0.261146243 1
 
< 0.1%
0.261449253 1
 
< 0.1%
ValueCountFrequency (%)
0.652144089 6
 
< 0.1%
0.645640994 13
0.1%
0.645475445 15
0.1%
0.644329005 6
 
< 0.1%
0.642881721 14
0.1%
0.641617746 15
0.1%
0.641480254 7
 
< 0.1%
0.640742728 3
 
< 0.1%
0.638787728 18
0.1%
0.63849268 1
 
< 0.1%

fruitmass
Real number (ℝ)

Distinct1515
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44655271
Minimum0.31192097
Maximum0.53566048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:18.076244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.31192097
5-th percentile0.38852652
Q10.41921572
median0.44657003
Q30.47413377
95-th percentile0.50487756
Maximum0.53566048
Range0.22373951
Interquartile range (IQR)0.054918046

Descriptive statistics

Standard deviation0.037035292
Coefficient of variation (CV)0.082935992
Kurtosis-0.56226926
Mean0.44655271
Median Absolute Deviation (MAD)0.027354306
Skewness-0.055531382
Sum6827.3444
Variance0.0013716129
MonotonicityNot monotonic
2023-05-14T14:18:18.133435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.446570029 101
 
0.7%
0.485989695 66
 
0.4%
0.497978979 65
 
0.4%
0.488638858 64
 
0.4%
0.504526655 60
 
0.4%
0.417947008 59
 
0.4%
0.460322355 58
 
0.4%
0.486889324 56
 
0.4%
0.463020067 55
 
0.4%
0.44252903 54
 
0.4%
Other values (1505) 14651
95.8%
ValueCountFrequency (%)
0.311920972 1
 
< 0.1%
0.320727305 10
0.1%
0.332220972 1
 
< 0.1%
0.335338738 10
0.1%
0.336240383 1
 
< 0.1%
0.342825548 6
 
< 0.1%
0.349353787 9
0.1%
0.352183472 1
 
< 0.1%
0.352186419 21
0.1%
0.352338278 1
 
< 0.1%
ValueCountFrequency (%)
0.535660479 8
0.1%
0.533820778 1
 
< 0.1%
0.532772006 4
 
< 0.1%
0.532222816 5
 
< 0.1%
0.530933466 14
0.1%
0.52979144 2
 
< 0.1%
0.529619103 14
0.1%
0.529501777 1
 
< 0.1%
0.529501569 9
0.1%
0.528522189 1
 
< 0.1%

seeds
Real number (ℝ)

Distinct2066
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.16495
Minimum22.079199
Maximum46.585105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:18.188634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22.079199
5-th percentile29.77828
Q133.232449
median36.040675
Q339.158238
95-th percentile42.934521
Maximum46.585105
Range24.505906
Interquartile range (IQR)5.9257884

Descriptive statistics

Standard deviation4.0310866
Coefficient of variation (CV)0.11146391
Kurtosis-0.5182314
Mean36.16495
Median Absolute Deviation (MAD)2.914771
Skewness0.015387122
Sum552925.93
Variance16.249659
MonotonicityNot monotonic
2023-05-14T14:18:18.553345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.98873363 96
 
0.6%
37.96686445 80
 
0.5%
31.92881551 71
 
0.5%
36.97636061 69
 
0.5%
35.92331365 61
 
0.4%
32.41787171 56
 
0.4%
35.82485206 54
 
0.4%
40.86547798 53
 
0.3%
35.11807556 52
 
0.3%
31.67018669 52
 
0.3%
Other values (2056) 14645
95.8%
ValueCountFrequency (%)
22.07919927 1
 
< 0.1%
23.41277571 13
0.1%
24.32062733 10
0.1%
24.60174115 1
 
< 0.1%
25.0423614 4
 
< 0.1%
25.43353016 4
 
< 0.1%
26.05469186 4
 
< 0.1%
26.10117938 12
0.1%
26.28235597 8
0.1%
26.48732245 8
0.1%
ValueCountFrequency (%)
46.58510536 4
 
< 0.1%
46.58510531 1
 
< 0.1%
46.36934409 8
0.1%
46.13942523 2
 
< 0.1%
46.12881916 1
 
< 0.1%
45.95298911 7
 
< 0.1%
45.80306982 12
0.1%
45.79441738 1
 
< 0.1%
45.718182 19
0.1%
45.61979691 3
 
< 0.1%

yield
Real number (ℝ)

Distinct776
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6025.194
Minimum1945.5306
Maximum8969.4018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2023-05-14T14:18:18.611332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1945.5306
5-th percentile3723.5234
Q15128.1635
median6117.4759
Q37019.6944
95-th percentile8090.4171
Maximum8969.4018
Range7023.8712
Interquartile range (IQR)1891.5309

Descriptive statistics

Standard deviation1337.0568
Coefficient of variation (CV)0.221911
Kurtosis-0.43656386
Mean6025.194
Median Absolute Deviation (MAD)931.39482
Skewness-0.29119496
Sum92119191
Variance1787721
MonotonicityNot monotonic
2023-05-14T14:18:18.673089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6251.61184 33
 
0.2%
7667.83619 32
 
0.2%
8538.462 32
 
0.2%
6528.79888 32
 
0.2%
6687.25926 32
 
0.2%
7557.46014 31
 
0.2%
4218.32799 31
 
0.2%
4575.76991 31
 
0.2%
5632.45917 31
 
0.2%
4228.81888 31
 
0.2%
Other values (766) 14973
97.9%
ValueCountFrequency (%)
1945.53061 26
0.2%
2379.90521 16
0.1%
2384.72892 17
0.1%
2452.68075 16
0.1%
2508.37567 15
0.1%
2605.69676 15
0.1%
2625.26916 21
0.1%
2688.02883 13
0.1%
2825.00374 29
0.2%
2946.92602 14
0.1%
ValueCountFrequency (%)
8969.40184 19
0.1%
8823.69011 16
0.1%
8743.52098 24
0.2%
8711.20896 19
0.1%
8671.71681 27
0.2%
8655.67644 16
0.1%
8652.04334 19
0.1%
8634.77583 16
0.1%
8621.81568 15
0.1%
8605.19995 10
 
0.1%

Interactions

2023-05-14T14:18:15.739240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:05.765906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.507237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.497943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.232012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.981233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.723246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.442674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.200543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.910003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.919635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.633040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.337932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.032681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.794900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:05.821899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.560646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.551110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.284693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.034219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.774195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.495857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.251202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.228581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.970565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.684282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.388252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.083467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.851133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:05.877859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.613990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.605945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.340712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.090151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.828961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.551013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.304623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.283613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.024344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.737858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.440641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.134837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.906007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:05.932081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.913346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.658751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.395091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.143679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.881118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.606789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.355598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.337767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.076721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.788689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.490680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.186311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.962868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:05.990289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.969951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.713105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.450322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.200339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.934345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.662923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.408463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.393399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.129122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.840864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.542195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.239283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.019802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.046883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.026286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.768382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.504223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.253617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.989065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.718967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.461478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.448124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.183307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.893186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.595006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.290235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.072035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.098927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.078224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.819521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.556661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.306036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.038136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.773351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.511341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.501154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.233050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.942832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.644316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.341213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.130008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.154095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.134248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.874934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.613469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.362561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.092006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.828774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.565498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.556166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.287565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.994849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.697038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.394937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.181840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.203305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.184275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.924069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.664031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.413902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.141840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.881756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.613624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.607406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.337059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.043875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.742687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.443905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.236873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.256319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.238537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.979705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.718780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.467437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.195923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.938964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.664569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.661730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.388998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.096336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.794152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.495311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.289622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.305306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.291045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.029458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.771373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.518623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.245119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.990169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.713360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.712514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.437206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.143633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.842563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.544395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.341017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.355535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.342856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.079107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.823303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.568654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.294121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.040697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.763119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.764729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.485719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.190427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.889172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.592941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.388770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.404353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.392108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.128132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.873735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.618523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.340129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.092272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.808633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.812288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.532675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.237210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.934908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.639096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:16.440075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:06.452624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:07.442398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.176995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:08.924848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:09.668016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:10.388146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.143594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:11.856314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:12.863097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:13.579922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.285927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:14.982044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-14T14:18:15.686496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-14T14:18:18.734930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
clonesizehoneybeebumblesandrenaosmiaMaxOfUpperTRangeMaxOfLowerTRangeMinOfLowerTRangeRainingDaysAverageRainingDaysfruitsetfruitmassseedsyieldMinOfUpperTRangeAverageOfUpperTRangeAverageOfLowerTRange
clonesize1.0000.8800.0940.0990.0420.0150.0150.0150.1670.167-0.404-0.365-0.387-0.3820.0220.0220.049
honeybee0.8801.0000.1310.2130.1490.0130.0130.0130.1360.134-0.314-0.281-0.301-0.2970.0000.0000.182
bumbles0.0940.1311.000-0.1720.109-0.001-0.001-0.001-0.069-0.0640.1610.1590.1720.1670.0240.0240.205
andrena0.0990.213-0.1721.0000.218-0.010-0.010-0.010-0.026-0.0250.0510.0510.0500.0550.0210.0210.021
osmia0.0420.1490.1090.2181.000-0.027-0.027-0.027-0.078-0.0730.1510.1450.1530.1500.0230.0230.046
MaxOfUpperTRange0.0150.013-0.001-0.010-0.0271.0001.0001.0000.0100.0090.0250.1580.068-0.0170.9570.9570.957
MaxOfLowerTRange0.0150.013-0.001-0.010-0.0271.0001.0001.0000.0110.0090.0250.1580.068-0.0170.9280.9280.928
MinOfLowerTRange0.0150.013-0.001-0.010-0.0271.0001.0001.0000.0110.0090.0250.1580.068-0.0170.9570.9570.957
RainingDays0.1670.136-0.069-0.026-0.0780.0100.0110.0111.0000.998-0.493-0.462-0.498-0.5040.0280.0290.029
AverageRainingDays0.1670.134-0.064-0.025-0.0730.0090.0090.0090.9981.000-0.489-0.459-0.494-0.5010.0290.0290.029
fruitset-0.404-0.3140.1610.0510.1510.0250.0250.025-0.493-0.4891.0000.9400.9410.8810.1310.1290.131
fruitmass-0.365-0.2810.1590.0510.1450.1580.1580.158-0.462-0.4590.9401.0000.9320.8230.1930.1920.193
seeds-0.387-0.3010.1720.0500.1530.0680.0680.068-0.498-0.4940.9410.9321.0000.8760.1410.1420.142
yield-0.382-0.2970.1670.0550.150-0.017-0.017-0.017-0.504-0.5010.8810.8230.8761.0000.0750.0750.075
MinOfUpperTRange0.0220.0000.0240.0210.0230.9570.9280.9570.0280.0290.1310.1930.1410.0751.0000.9280.928
AverageOfUpperTRange0.0220.0000.0240.0210.0230.9570.9280.9570.0290.0290.1290.1920.1420.0750.9281.0000.928
AverageOfLowerTRange0.0490.1820.2050.0210.0460.9570.9280.9570.0290.0290.1310.1930.1420.0750.9280.9281.000

Missing values

2023-05-14T14:18:16.524479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-14T14:18:16.652468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

clonesizehoneybeebumblesandrenaosmiaMaxOfUpperTRangeMinOfUpperTRangeAverageOfUpperTRangeMaxOfLowerTRangeMinOfLowerTRangeAverageOfLowerTRangeRainingDaysAverageRainingDaysfruitsetfruitmassseedsyield
025.0000.5000.2500.7500.50069.70042.10058.20050.20024.30041.20024.0000.3900.4250.41832.4614476.811
125.0000.5000.2500.5000.50069.70042.10058.20050.20024.30041.20024.0000.3900.4450.42233.8585548.122
212.5000.2500.2500.6300.63086.00052.00071.90062.00030.00050.80024.0000.3900.5530.47138.3426869.778
312.5000.2500.2500.6300.50077.40046.80064.70055.80027.00045.80024.0000.3900.5660.47839.4686880.776
425.0000.5000.2500.6300.63077.40046.80064.70055.80027.00045.80024.0000.3900.5800.49440.4857479.934
525.0000.5000.2500.6300.75094.60057.20079.00068.20033.00055.90034.0000.5600.5650.48440.5557267.283
612.5000.2500.3800.5000.63086.00052.00071.90062.00030.00050.80024.0000.3900.4990.44235.5185739.680
712.5000.2500.2500.7500.75086.00052.00071.90062.00030.00050.8001.0000.1000.6200.53042.1917920.062
825.0000.5000.3800.3800.75094.60057.20079.00068.20033.00055.90016.0000.2600.5330.46536.1666465.372
925.0000.5000.2500.6300.63094.60057.20079.00068.20033.00055.90034.0000.5600.3400.38228.7643519.431
clonesizehoneybeebumblesandrenaosmiaMaxOfUpperTRangeMinOfUpperTRangeAverageOfUpperTRangeMaxOfLowerTRangeMinOfLowerTRangeAverageOfLowerTRangeRainingDaysAverageRainingDaysfruitsetfruitmassseedsyield
1527912.5000.2500.2500.3800.50094.60057.20079.00068.20033.00055.9001.0000.1000.5340.45436.3865934.011
1528025.0000.5000.2500.5000.50094.60057.20079.00068.20033.00055.90034.0000.5600.2830.35627.6222825.004
1528125.0000.5000.2500.6300.75077.40046.80064.70055.80027.00045.80024.0000.3900.4800.42034.0295794.892
1528225.0000.5000.2500.6300.63094.60057.20079.00068.20033.00055.90016.0000.2600.4430.42533.2855042.640
1528325.0000.5000.2500.7500.50077.40046.80064.70055.80027.00045.80034.0000.5600.4890.44834.8395496.085
1528412.5000.2500.2500.3800.50077.40046.80064.70055.80027.00045.80016.0000.2600.5560.47640.5467667.836
1528512.5000.2500.2500.2500.50086.00052.00071.90062.00030.00050.80034.0000.5600.3540.38829.4673680.560
1528625.0000.5000.2500.3800.75077.40046.80064.70055.80027.00045.80034.0000.5600.4230.41732.2994696.444
1528725.0000.5000.2500.6300.63069.70042.10058.20050.20024.30041.20024.0000.3900.5420.43436.6746772.933
1528825.0000.5000.2500.6300.50077.40046.80064.70055.80027.00045.80016.0000.2600.4920.44735.0955867.997

Duplicate rows

Most frequently occurring

clonesizehoneybeebumblesandrenaosmiaMaxOfUpperTRangeMinOfUpperTRangeAverageOfUpperTRangeMaxOfLowerTRangeMinOfLowerTRangeAverageOfLowerTRangeRainingDaysAverageRainingDaysfruitsetfruitmassseedsyield# duplicates
012.5000.2500.2500.2500.75069.70042.10058.20050.20024.30041.20016.0000.2600.5460.45237.8076922.8472
112.5000.2500.2500.5000.75094.60057.20079.00068.20033.00055.9001.0000.1000.6330.51944.2798318.7992
212.5000.2500.3800.5000.75077.40046.80064.70055.80027.00045.8001.0000.1000.6420.53045.7188743.5212
325.0000.5000.2500.6300.63086.00052.00071.90062.00030.00050.80016.0000.2600.5140.45536.6836157.0552
425.0000.5000.3800.6300.50086.00052.00071.90062.00030.00050.80034.0000.5600.3640.39129.7403631.9052
537.5000.7500.2500.2500.25077.40046.80064.70055.80027.00045.80034.0000.5600.2840.35226.1012625.2692
637.5000.7500.2500.2500.25086.00052.00071.90062.00030.00050.80034.0000.5600.3540.38829.3733712.9982